In this paper a new application-oriented method for automatic Farsi license plate detection (ALPD), based on morphology and a modified edge clustering algorithm is proposed. Access control (AC), law enforcement (LE), and road patrol (RP) are mainly three applications for ALPD. After image enhancement by preprocessing, the edge statistics analysis and the morphology filter are used to decrease the search regions and remove the unwanted edges. Then the expectation-maximization (E-M) algorithm is used to estimate the corresponding Gaussian components for edges of remained regions. In this way the results of edge clustering and Gaussian components estimation are deterministic whereas the processing time in comparison with similar approaches in literature, is decreased significantly. Candidate regions are obtained by applying application-oriented thresholds to the properties of estimated Gaussian components. Finally for each candidate region, the maximally stable extremal region (MSER) detector is used to detect character-like regions and then select the region(s) of interest containing license plates. The proposed method is evaluated by using a database which includes images for the three groups AC, LE and RP applications, whereas some images suffer of being low quality, low contrast and blur and some images have complex background through existing multiple license plates. The experimental results show that our proposed method is reliable for images of different quality and illumination condition and it is able to detect the rotated and skewed license plates even in images containing multiple license plates and complex backgrounds.

Early identification of diabetic retinopathy is highly beneficial for preventing the progression of disease. Appearance of blood vessels & retinal surface is a good ophthalmological sign of diabetic retinopathy in fundus images. In this paper, a novel method involving two approaches has been proposed for diagnosis of diabetic retinopathy. The first approach deals with estimation of fractal dimension of lesions by applying power spectral fractal dimension algorithms. For healthy retinas, fractal dimensions are found to be in the range of 2.00 to 2.069, whereas for retinas with diabetic retinopathy, fractal dimensions exceed upper limit. In the second approach, Gray Level Co-occurrence Matrix method is used to analyze the extracted regions from healthy and diabetes affected fundus retinal images. Texture features such as entropy & contrast are computed for healthy and unhealthy regions. These texture features are compared with fractal dimensions. The authors observed positive correlation between entropy and fractal dimensions, whereas negative correlation with contrast and fractal dimensions. Detailed implementations of the proposed work are presented.

Leaves images segmentation is an important task in the automated plant identification. Images leaf segmentation is the process of extracting the leaf from its background, which is a challenging task. In this paper, we propose an efficient and effective new approach for leaf image segmentation, we aim to separate the leaves from the background and from their shadow generated when the photo was taken. The proposed approach calculates the local descriptors for the image that will be classified for the separation of the different image's region. We use Pseudo Zernike Moments (PZM) as a local descriptor combined with K-means algorithm for clustering. The efficient of PZM for features extraction lead to very good results in very short time. The validation tests applied on a variety of images, showed the ability of the proposed approach for segmenting effectively the image. The results demonstrate a real improvement compared to those of new existing segmentation method.

The aim here remains to introduce effectiveness of interval methods in analyzing dynamic uncertainties for marine navigational sensors. The present work has been carried out with an integrated sensor suite consisting of a low cost MEMs inertial sensor, GPS receiver of moderate accuracy, Doppler velocity profiler and a magnetic fluxgate compass. Error bounds for all the sensors have been translated into guaranteed intervals. GPS based position intervals are fed into a forward-backward propagation method in order to estimate interval valued inertial data. Dynamic noise margins are finally computed from comparisons between the estimated and measured inertial quantities It has been found that the intervals as estimated by proposed approach are supersets of 95% confidence levels of dynamic errors of accelerations. This indicates a significant drift of dynamic error in accelerations which may not be clearly defined using stationary error bounds. On the other side bounds of non-stationary error for rate gyroscope are found to be in consistence with the intervals as predicted using stationary noise coefficients. The guaranteed intervals estimated by the proposed forward backward contractor, are close to 95% confidence levels of stationary errors computed over the sampling period.

In this paper we aim to solve a problem of image reconstruction in tomography. In medical imaging, patients suffer from taking high dose of radioactive drug in order to get a well-qualified image. Our goal is to reduce this dose of radioactive drug given to the patients in PET scan and to get a well-qualified image. We use to modeling this problem using a convex function to minimize. In tomography, real problem requires a positive constraint and may get a blurred image due to poisson noise. Then, in order to get back a non blurred image of human body, we add to this function a wavelet regularization which is a non differentiable function. We introduce specific algorithms to get the minimum of the global function obtained. After presenting the classic algorithms with their conditions to solve the problem we find that Chambolle Pock's algorithm requires less properties than these algorithms and gives good results. Then, we propose its computation method with the proof.

Image reconstruction is the process of generating an image of an object from the signals captured by the scanning machine. Medical imaging is an interdisciplinary field combining physics, biology, mathematics and computational sciences. This paper provides a complete overview of image reconstruction process in MRI (Magnetic Resonance Imaging). It reviews the computational aspect of medical image reconstruction. MRI is one of the commonly used medical imaging techniques. The data collected by MRI scanner for image reconstruction is called the k-space data. For reconstructing an image from k-space data, there are various algorithms such as Homodyne algorithm, Zero Filling method, Dictionary Learning, and Projections onto Convex Set method. All the characteristics of k-space data and MRI data collection technique are reviewed in detail. The algorithms used for image reconstruction discussed in detail along with their pros and cons. Various modern magnetic resonance imaging techniques like functional MRI, diffusion MRI have also been introduced. The concepts of classical techniques like Expectation Maximization, Sensitive Encoding, Level Set Method, and the recent techniques such as Alternating Minimization, Signal Modeling, and Sphere Shaped Support Vector Machine are also reviewed. It is observed that most of these techniques enhance the gradient encoding and reduce the scanning time. Classical algorithms provide undesirable blurring effect when the degree of phase variation is high in partial k-space. Modern reconstructions algorithms such as Dictionary learning works well even with high phase variation as these are iterative procedures.

Block matching algorithm (BMA) based motion estimation (ME) is most accepted method for removal of temporal redundancy between frames in video coding. With recent advancement in resolution of video, the need of search pattern covering most of macroblocks within search area in frame is increasing. Existing search patterns are tiny and take plenty of time to reach at edge or corner of the search window. With aim of covering nearly every probable candidate macroblocks in all direction and to speed up the search process, multipoint search patterns are presented in this paper. Initial candidate macroblocks are chosen on grid of 12x12 and then search progresses like traditional diamond or hexagon search. Due to multipoint, chances of trapping in incorrect direction is very less and method can exhibit better quality of encoding with optimum number of search points.

Recently nature inspired metaheuristic algorithms have been applied in image enhancement field to enhance the low contrast images in a control manner. Bat algorithm (BA) and Firefly algorithm (FA) is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been implemented with the help of chaotic sequence and lévy flight. One of them is FA via lévy flight where step size of lévy flight has been taken from chaotic sequence. In the Bat algorithm the local search has been done via lévy flight with chaotic step size. Chaotic sequence shows ergodicity property which helps in better searching. These two algorithms have been applied to optimize parameters of parameterized high boost filter. Entropy, number of edge pixels of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it is clear that BA with chaotic lévy outperforms the FA via chaotic lévy.